Clustering of Movie Taglines over Time Marc A. LeMoine Northwestern University 435-CIS_SEC61 Introduction to Predictive Analytics & Data Collection August 24‚ 2014 Executive Summary A reproach of Dr. Miller’s initial study on historical movie taglines. This follow-up analysis considered movie taglines between 1979 and 2014 which relates to my own personal “movie watching years”. The goal was to employ additional strategies including stemming and looking at various combinations of clustering algorithms
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Semi-Supervised K-Means Clustering for Outlier Detection in Mammogram Classification K. Thangavel1‚ A. Kaja Mohideen2 Department of Computer Science‚ Periyar University‚ Salem‚ India 1 drktvelu@yahoo.com‚ 2kaja.akm@gmail.com Abstract— Detection of outliers and relevant features are the most important process before classification. In this paper‚ a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted
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ABSTRACT This paper presents an approach for image segmentation using pillar K-Means algorithm. In this paper the segmentation process includes a mechanism for clustering the elements of high resolution images. By using this process we can improve precision and reduce computational time. The system applies K-means clustering to image segmentation after optimized by pillar algorithm. The pillar algorithm considers that pillars placement should be located as far as possible from each other
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Non-Hierarchical Cluster Analysis Non-hierarchical cluster analysis (often known as K-means Clustering Method) forms a grouping of a set of units‚ into a pre-determined number of groups‚ using an iterative algorithm that optimizes a chosen criterion. Starting from an initial classification‚ units are transferred from one group to another or swapped with units from other groups‚ until no further improvement can be made to the criterion value. There is no guarantee that the solution thus obtained
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us/en/business/d/business~solutions~whitepapers~en /Documents~hadoop-introduction.pdf.aspx [4] Storage Conference. The Hadoop Distributed File System http://storageconference.org/ 2010/ Papers/ MSST/Shvachko.pdf [5] A Tutorial on Clustering Algorithms. K-Means Clustering http://home.dei.polimi.it/matteucc/ Clustering/ tutorial_html/kmeans.html [6] International Journal of Computer Science Issues. Setting up of an Open Source based Private Cloud http://ijcsi.org/papers/IJCSI-8-3-1-354-359.pdf [7] Eucalyptus. Modifying a
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Analysis for Color Image Segmentation of Natural Scenes by K-means Based Clustering Jia Song(1‚∗) ‚ Eva M. Valero(1) ‚ Juan L. Nieves(1) 1. Optics Department‚ Faculty of Science‚ University of Granada‚ Spain (∗) Corresponding author Email: songjia815@gmail.com Recibido / Received: dd/mm/yyyy Aceptado / Accepted: dd/mm/yyyy ABSTRACT: In this paper‚ we propose to segment color images of natural scenes by pixel clustering in different color spaces in order to compare the performances of the color
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Abstract In this project our main objective is to detect the tumor and calculate its area based on classifying the images. Death rate among people has been increased due to diseases like brain tumor. The brain tumor that can be identified by using image processing. The proposed system enables automatic detection of brain tumor through MRI. So our main objective is to study‚ analyze and enhance MRI Image from the existing algothrim. Certain traditional approach requires manually extracting the
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Optimal Classifier Based Spectrum Sensing in Cognitive Radio Wireless Systems Siddharth Sharma Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur - 208016‚ India +91-9997773460 Aditya K. Jagannatham Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur - 208016‚ India +91-512-2597494 sharmas@iitk.ac.in ABSTRACT In this work‚ we present and investigate the performance of novel classification schemes for spectrum sensing in cooperative
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P. (2001). Principles of Data Mining (Adaptive Computation and Machine Learning) [Handcock et al.‚ 2003] Handcock‚ M. S.‚ Hunter‚ D. R.‚ Butts‚ C. T.‚ Goodreau‚ S. M.‚ and Morris‚ M [Hennig‚ 2010] Hennig‚ C. (2010). fpc: Flexible procedures for clustering. R package version 2.0-3. [Hornik et al.‚ 2012] Hornik‚ K.‚ Rauch‚ J.‚ Buchta‚ C.‚ and Feinerer‚ I. (2012). textcat: N-Gram Based Text Categorization [Hothorn et al.‚ 2012] Hothorn‚ T.‚ Buehlmann‚ P.‚ Kneib‚ T.‚ Schmid‚ M.‚ and Hofner‚ B. (2012)
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SEMINAR REPORT ON DATA CLUSTERING Submitted by NITIN PAUL Semester 7 Computer Science &Engineering Univ Roll No:07400038 To The University of Kerala In partial fulfilment of the requirements for the award of the degree Of Bachelor of Technology in Computer Science and Engineering DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
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